Indoor environmental quality and infection control in surgery rooms: Code requirements vs. performance motivation. A critical review
Bibliographic record
Abstract
Surgery rooms are a space type with particularly stringent indoor environmental quality (IEQ) requirements (large airflow rates and narrow comfort windows), which translate into high energy use. Due to the unclear IEQ and infection control requirements for surgery rooms in Spain, these spaces are often designed and operated 24 hours per day and 7 days per week, to meet the most stringent recommendations (not only the requirements) in the available standards and guidelines. This paper critically reviews the Spanish mandatory requirements for surgery rooms by comparing them against their performance motivation and other international standards. Regulatory ambiguities and code-compliant energy efficiency opportunities are identified.The requirements and recommendations in the standards included in this review differ in their magnitude (particularly the airflow requirements), but are similar in their prescriptive nature. This paper identifies the performance goals associated to the prescriptive requirements, and proposes a method to adjust system operation (outdoor airflow rate, total supply air, indoor air temperature, and indoor air relative humidity) to meet IEQ performance goals while reducing energy use. Further work is required to define operation infection control requirements for the different surgery types and enable a performance based control strategy based on real time particle concentration monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".